def y_transform(self, record: TubRecord):
angle: float = record.underlying['user/angle']
throttle: float = record.underlying['user/throttle']
angle = linear_bin(angle, N=15, offset=1, R=2.0)
throttle = linear_bin(throttle,
N=20,
offset=0.0,
R=self.throttle_range)
return angle, throttle
def lstm_get_record_gen(self, record_transform=None, df=None):
pd.set_option('max_colwidth', 10)
num_steps = 30
batch_size = 20
current_idx = 0
skip_step = 30
if df is None:
df = self.get_df()
data_size = df.shape[0]
df.sort_index(inplace=True)
while True:
for i in range(batch_size):
if current_idx + num_steps >= data_size:
# reset the index back to the start of the data set
current_idx = 0
#print(df[current_idx:current_idx + num_steps])
record_dict = df[current_idx:current_idx +
num_steps].to_dict(orient='record')[0]
record_dict = self.read_record(record_dict)
current_idx += skip_step
record_dict["user/angle"] = utils.linear_bin(
record_dict["user/angle"])
#print("-----")
#print(record_dict)
yield record_dict
def __getitem__(self, index):
count = 0
records = []
images = []
angles = []
throttles = []
is_inferred = type(self.keras_model) is KerasInferred
is_categorical = type(self.keras_model) is KerasCategorical
is_resnet18_categorical = type(
self.keras_model) is Resnet18CategoricalKeras
while count < self.batch_size:
i = (index * self.batch_size) + count
if i >= len(self.records):
break
record = self.records[i]
record = self._transform_record(record)
records.append(record)
count += 1
for record in records:
image = record['cam/image_array']
angle = record['user/angle']
throttle = record['user/throttle']
images.append(image)
# for categorical convert to one-hot vector
if is_categorical or is_resnet18_categorical:
R = self.config.MODEL_CATEGORICAL_MAX_THROTTLE_RANGE
angle = linear_bin(angle, N=15, offset=1, R=2.0)
throttle = linear_bin(throttle, N=20, offset=0.0, R=R)
angles.append(angle)
throttles.append(throttle)
X = np.array(images)
if is_inferred:
Y = np.array(angles)
else:
Y = [np.array(angles), np.array(throttles)]
return X, Y
def y_transform(self, record: TubRecord):
angle: float = record.underlying['user/angle']
throttle: float = record.underlying['user/throttle']
angle = linear_bin(angle, N=15, offset=1, R=2.0)
throttle = linear_bin(throttle, N=20, offset=0.0, R=0.5)
return {'angle_out': angle, 'throttle_out': throttle}